[ Ссылка ] -- The data preparation process is an integral part of using data to generate business intelligence. Good data preparation is vital to getting results, as it helps eliminate errors in analysis, makes the analysis process more efficient, and makes prepared data more accessible to all stakeholders. It’s crucial to understand all the component parts of preparing data and to select the right tools for data preparation to ensure that this crucial step gets accomplished without a hitch.
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Why? Today’s businesses depend on data analysis to generate actionable business intelligence. This can then be used for a variety of purposes, including crafting business plans and strategies, improving customer relations, and fine-tuning products and services. While collecting data is a pivotal part of the process, it’s crucial to prepare the data the right way beforehand, so it can be directly fed into data analysis and BI tools to commence the analysis process.
Let us take a deep dive into the data preparation process, look at some of the important variables, and pay attention to some fundamental things to keep in mind.
The Basics of Data Preparation
The process of data preparation entails cleaning, organizing, and transforming raw data before it can be sent for processing and eventual analysis.
Depending on the kind and volume of data you are working with, this can involve combining multiple sets of data into larger, more integrated volumes, making corrections to the data wherever needed, and reformatting the data to make it easier for processing and analysis tools to access it later.
Data preparation can be a long, tedious process. However, it cannot be overlooked, as it has a direct impact on the integrity of the results of data analysis. It is a crucial first step if you want to preserve high standards of data quality, eliminate any kind of data bias, and properly contextualize the data so that it can be turned into valuable business insight.
The process of data preparation, in addition to standardizing the data format, also enriches the source data or removes fringe data elements to enhance data quality. If you want quality results from your analysis and BI tools, any errors, missing values, and other inaccuracies need to be put to bed during the data preparation process.
The final step of data preparation is to store the processed data into the right data repository, which can be a data lake, a data warehouse, or a NoSQL style database.
The preparation work itself is usually tackled by IT teams, data management teams, and BI teams. However, data analysts can also make use of self-service data prep tools to prepare the data by themselves. In such cases, specific data sets can also be curated for self-service BI tools for analysts and other users.
What are the main processes of data preparation?
The data preparation process includes several stages, serving important purposes when it comes to cleaning, preparing, and categorizing the data for future use. While there might be subtle variations in these stages, depending on the use case and the type of data involved, the basic stages are generally the same. Here’s a quick overview.
Collection
This is the initial stage of the process where data is gathered and brought to a central location from disparate sources. This can include data lakes and warehouses, operational systems, and other sources of data. This stage is also a great time for data analysts and BI team members to take a first look at the data and to decide whether it is an overall good fit for the application that it is destined for. Great care needs to go into this step as it’s easy to assume that data from a trusted source will always be quality data, but that is not always the case.
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About NextPhase.ai
NextPhase.ai is a data cloud services provider specializing in Snowflake, cloud data management and analytics technologies. We accelerate enterprise digital transformation initiatives by leveraging our innovative cloud data management technology, “NextPhase.ai DATAFLO” to optimize and rationalize disparate enterprise data into relevant insights. “DATAFLO” is designed to automate the lifecycle of data management transformation using AI and ML along with expeditious on-ramps to the Snowflake data cloud infrastructure. NextPhase.ai provides a range of technology consulting services for the Financial Services, Biotech and Technology industry sectors combining our platform-based services, seasoned talent, and industry proven methodology so our customers can harness more from their data. We are a Silicon Valley based company with global presence having delivered high value service engagements for numerous Global 2000 enterprises.
